“More time is spent researching, analysing results and gleaning insights, than communicating the results” – discuss.

That detracts from the impact of research, and doesn’t help answer the important question – “So what?”.
Visualising_data helps to –

  • uncover the not so obvious
  • deepen understanding
  • communicate findings
  • and engage stakeholders.

Visualisations can be wonderfully imaginative, with big data only adding to the wow factor. But UR usually produces small datasets, and low tech’ visualisations.

Ways of presenting small data –

A questionnaire that asks Likert rating questions (strongly agree… strongly disagree), yields quantitative data suited to bar graphs.

Visualisation of the results from a Likert survey question in a bar graph

Visual summary of survey results to a Likert rating question – http://peltiertech.com/diverging-stacked-bar-charts/

Quantitative data helps put such results in perspective and give an overview. However,insights can still be gained, when the sample size (n) isn’t large enough to be statistically meaningful.

The graph below didn’t result from a large longitudinal study, but a small contextual inquiry around hospital appointment cards. Nonetheless, it illustrated a holistic, patient centred view, of multiple service delivery which primary care staff hadn’t seen before.

Visualisation of patient contact with health services

Visualising patient contact with health services over an 18 months period – horizontal lines represent referrals.

Another bar chart shows the results of rewording the questions of a satisfaction survey. The new instructions were understood more quickly and the last, free text question invited longer answers.

Comparison of time taken to complete two satisfaction surveys with slightly different wording

The effect of re-wording the questions (x axis) of a short satisfaction survey upon, completion time (y axis).


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